Disruptors and their applicability to Next Generation Analytic Platforms How to embed disruptors in your business strategy? October 6 th , 2015 Ashish Verma, Hybrid Services and Innovation Leader, Deloitte Consulting LLP
Agenda 1. An Unprecedented Opportunity 2. The Data Management Life Cycle 3. How disruptors are impacting industries? 4. Organizing to Succeed
While not necessarily new…an unprecedented opportunity Evolution not revolution Confluence of advances The market is still lead to enormous emerging and presents breakthrough potential an enormous opportunity
Technology disruptors continue to have the impact on the business of tomorrow; today Disruptors Market momentum is rapidly growing : Big Data 200+TB of stored data in every sector 60 billion intelligent devices with a forecast of 26 billion Real Time Decisioning connected devices by 2020 1 Industry players with their own themes Predictive Analytics Cisco: “Internet of Everything - $14.6 trillion value at - stake by 2022” Cloud GE: “Industrial Internet + analytics” - IBM: “Smarter Planet” - Cyber Security and Privacy Rapidly forming ecosystem offerings and partnerships due to early stage of maturity In Memory - Cloudera, Intel Partnership, May 2014 - EMC Pivotal along with GE, Intel, Accenture, AT&T, Cognitive Computing Cisco. September 2013 - IBM and Technicolor IoT and M2M cloud solution, Jan 2014 Machine Learning - AT&T & Qualcomm to enable and connect consumer IoT devices, Jan 2014 IoT Wearables Sources: 1) Gartner, Nov. 2013
Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant Disruptor Key Business Triggers Key Technology • Handling data volumes that are more than 10 TB Hadoop • Data with a changing structure, or no structure at all Cloudera • Very high throughput systems, with millions of concurrent users and HortonWorks Big Data thousands of queries per second IBM Big Insights • Business requirements that differ from the relational database model, Oracle Big Data Appliance for example swapping ACID (Atomicity, Consistency, Isolation, NoSQL Data Stores i.e. MongoDB, Durability) for BASE (Basically Available, Soft State, Eventually Cassandra Consistent) • Processing of machine learning queries that are inefficient or impossible to express using SQL • Increase service velocity for the business by embedding analytics into Apache Kafka the operational processes to support frontline decision making based Apache Storm on real-time events Apache Spark Real Time • Provide a mechanism to route and correlate events in real time even SAP Real Time Offer Management Decisioning in scenarios of large volumes of data Oracle Real Time Decisions • Predictive techniques enable strategic decision making by providing SAS Predictive Analytics future insights based on large volumes of structured and un- SalesForce (Analytics) Wave Cloud structured data. Examples include forecasting sales effectiveness by IBM SPSS forecasting customer behavior, forecasting product demand, etc. RapidMiner Predictive Oracle Advanced Analytics Analytics Oracle Visual Analyzer SAP Visual Insights R
Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant Disruptor Key Business Triggers Key Technology • Rapid implementation: Less time is required to get up and running Amazon Web Services on cloud-based systems Microsoft Azure • Cost predictability: Cloud’s pay -as-you-go model makes it easier to Dimension Data Cloud predict IT costs Google Cloud • Balanced ROI: Cloud delivers a faster return on IT investments, IBM Big Insights on Cloud thanks to accelerated implementation and elimination of upfront HP Cloud Analytics licensing and infrastructure costs Bluelock • Agility: Companies can quickly develop and deploy new IT Salesforce.com capabilities and business processes to stay ahead of the competition and keep pace with changes in the marketplace • Scalability: Cloud provides a flexible platform that can grow or shrink as needed, enabling businesses to explore new markets, pursue new innovations and serve new customer segments • Threat Awareness: Automated network and malware forensic Identity, Credential, and Access analysis are needed, as well as intelligence collection from honeypots Management(ICAM) solutions or other ‘baiting’ operations Security Information & Event Cyber Security • Security Intelligence & Event Management Solutions: Detailed Management (SIEM) solutions & Privacy logging and SIEM are also table stakes when it comes to building advanced cyber-threat management capabilities. The stream of event data, when combined with internal and external intelligence, can allow correlation, analysis, and subsequent detection of threats that would otherwise go unnoticed • Unstructured and semi-structured inputs and intelligence : Invest in data collection and analysis solutions — allowing automated crawling and information parsing. • Use cyber analytics — linked to threat rosters and known business risks and fraud issues — to identify potential areas of escalating risk
Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant Disruptor Key Business Triggers Key Technology • Reduce total cost of ownership because the shift from physical to Oracle Exalytics In-Memory Machine logical reduces the hardware footprint, allowing more than 40 times SAP HANA the data to be stored in the same finite space Kognitio In Memory • Thousand-fold improvement in query response times to transaction Apache Spark processing speed increases of 20,000 times • Crunch massive amounts of data in real time to improve relationships with their customers • In-memory responses are also more predictable, able to handle large volumes and a mix of structured, semi-structured, and unstructured raw data • Operating costs can also be cut both by reducing maintenance needs and by streamlining the performance of employees using the technology • Industries wrestling with massive amounts of unstructured data or IBM Watson struggling to meet growing demand for real-time visibility should Cognitive Scale consider taking a look. Cognitive analytics can be a powerful way to bridge the gap between the intent of big data and the reality of practical decision making • As the demand for real-time support in business decision making intensifies, cognitive analytics will likely move to the forefront in high- Cognitive stakes sectors and functions Analytics • It can improve prediction accuracy, provide augmentation and scale to human cognition, and allow tasks to be performed more efficiently (and automatically) via context-based suggestions
Within every organization understanding and applying disruptors to key Business Triggers is critical to staying relevant Disruptor Key Business Triggers Key Technology • Applications of machine learning vary in complexity, from simplistic Mahout spam filters in emails to more complex forms such as the virtual SAS employee that can function as a service i.e. desk employee in retail R Machine and customer care operations. Learning • These applications are aided by technologies such as natural language processing, voice recognition, handwriting recognition, image processing, correlation analytics and quantum computing • A whole range of products and services built on underlying technology such as IBM’s Watson that can act as ‘Smart Advisors’ • Support sensor driven decision analytics Wireless technologies (WiFi, Bluetooth, RFID) • Provide product life extension (enabling product upgrades and enhancements delivered via software commands) and Sensors IoT automated support that significantly reduces costs Cloud Storage and Processing • Provide process improvements through continuous precise Platforms with Machine Learning and adjustments in manufacturing lines Advanced Modeling Capabilities • Optimize resource consumption across networks • Wearables value comes from introducing technology into previously Google Glass prohibitive environments — where safety, logistics, or even etiquette mHealth have constrained traditional technology solutions Fitness & Activity trackers Wearables • Wearables generate data in real time and intelligently push it to a Smartwatches devices according to the user’s current context — just-in-time digital logistics. Such use cases suggest that wearables may be most valuable in an organization’s operations, rather than in customer - facing applications • Wearables can be the first seamless way to enable workers with digital information — especially where hands-free utility offers a clear advantage. Using wearables, workers in harsh environmental conditions can access data without removing gloves or create records without having to commit data to memory and then moving to sheltered workstation
Agenda 1. An Unprecedented Opportunity 2. The Data Management Life Cycle 3. How disruptors are impacting industries? 4. Organizing to Succeed
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